Order Effects of Resistance and Stretching Exercises on Heart Rate Variability and Blood Pressure in Healthy Adults
Bibliographic record
Abstract
da Silva Araujo, G, Behm, DG, Monteiro, ER, de Melo Fiuza, AGF, Gomes, TM, Vianna, JM, Reis, MS, and da Silva Novaes, J. Order effects of resistance and stretching exercises on heart rate variability and blood pressure in healthy adults. J Strength Cond Res 33(10): 2684-2693, 2019-The purpose of this study was to compare the acute effect of different combinations and order of resistance exercise (RE) and stretching exercise (SE) on heart rate variability, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Twenty subjects, recreationally trained, performed 5 sessions in a random order: (a) SE followed by RE no rest (SE + RE), (b) RE followed by SE no rest (RE + SE), (c) SE between RE sets (SBE), (d) SE isolated, and (e) RE isolated. Heart rate variability, SBP, and DBP were collected for 15 minutes before (baseline) and 60 minutes after each experimental session. A significant decrease was found for standardized deviation of differences between adjacent normal r-r intervals (RMSSDms) SE + RE (-50.79%), SE (+9.2%), SBE (-42.8%), and RE (-46.3%). Similarly, a significant increase was found for LFnu in SE + RE (+12.8%) and SBE (+16.6%). In addition, a significant decrease was found for HFnu in SE + RE (-34.8%) and SBE (-39.7%). Finally, a significant decrease was found for SBP in SE (-6.1%). In conclusion, this study indicated that SE followed by RE promotes a reduction in LFnu and RMSSDnu, with trivial to small magnitude decreases in SBP. Therefore, performing SE either before or between RE would be an important exercise prescription recommendation to lower cardiac load and consequently greater safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".